PAPER2LLM++: Continual Self-Evolution of LLMs from Research Papers
Research on LLMs continually uncovers model limitations, their causes, and potential solutions. Yet these human discoveries remain largely disconnected from model evolution: an LLM does not automatically learn from new research about its own failures. We introduce PAPER2LLM++, a framework for continual self-evolution of LLMs from research papers. Rather than treating papers merely as knowledge to retrieve, PAPER2LLM++ uses the growing literature as a stream of evidence and supervision for model improvement. For each incoming paper, it extracts evidence-grounded findings, tests whether the reported limitation persists in the current model, and, when needed, converts the findings into candi...
| Authors | Hongji Pu, Yilun Zhao, Wenpeng Yin |
|---|---|
| Primary category | cs.AI |
| Published | 2 October 2026 |
| Last updated at source | 5 October 2026 |
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